Robotic grasping is still challenging due to limitations in perception and control, especially when the CAD models of objects are unknown. Although some grasp planning approaches using computer vision have been proposed, these methods can be seen as open-loop grasp planning methods and are often not robust enough. In this paper, a novel grasp planning method combining CNN-based quality prediction and closed-loop control (CNNB-CL) is proposed for a vacuum gripper. A large-scale dataset is generated for CNN training, which contains more than 2.3 million synthetic grasps and their grasp qualities evaluated by grasp simulations with 3D models. Unlike other neural networks which predict grasp success by assigning a binary value or grasp quality level by assigning an integer value, the proposed CNN predicts the grasp quality via a linear regression architecture. Additionally, the method adjusts the grasp strategies and detects the optimal grasp based on feedback from a force-torque sensor. Various simulations and physical experiments prove that the CNNB-CL method is robust for random noise disturbance in observation and compatible with different depth cameras and vacuum grippers. The proposed method finds the optimal grasp from 2,000 candidates within 300 ms and achieves a 92.18% average success rate for different vacuum grippers, which outperforms the state-of-the-art methods regarding success rate and robustness.

由于感知和控制方面的限制，机器人抓取仍然具有挑战性，尤其是当物体的 CAD 模型未知时。尽管已经提出了一些使用计算机视觉的抓取规划方法，但这些方法可以被视为开环抓取规划方法，并且通常不够稳健。在本文中，提出了一种基于 CNN 的质量预测和闭环控制 (CNNB-CL) 相结合的新型抓取规划方法，用于真空抓手。为 CNN 训练生成了一个大规模数据集，其中包含超过 230 万个合成抓取及其通过 3D 模型抓取模拟评估的抓取质量。与其他通过分配二进制值来预测抓取成功或通过分配整数值来预测抓取质量水平的神经网络不同，所提出的 CNN 通过线性回归架构预测抓取质量。此外，该方法根据来自力-扭矩传感器的反馈调整抓握策略并检测最佳抓握。各种模拟和物理实验证明 CNNB-CL 方法对观察中的随机噪声干扰具有鲁棒性，并兼容不同的深度相机和真空夹具。所提出的方法在 300 ms 内从 2,000 名候选者中找到了最佳抓握，并且对不同的真空夹具实现了 92.18% 的平均成功率，这在成功率和鲁棒性方面优于最先进的方法。各种模拟和物理实验证明 CNNB-CL 方法对观察中的随机噪声干扰具有鲁棒性，并兼容不同的深度相机和真空夹具。所提出的方法在 300 ms 内从 2,000 名候选者中找到了最佳抓握，并且对不同的真空夹具实现了 92.18% 的平均成功率，这在成功率和鲁棒性方面优于最先进的方法。各种模拟和物理实验证明 CNNB-CL 方法对观察中的随机噪声干扰具有鲁棒性，并兼容不同的深度相机和真空夹具。所提出的方法在 300 ms 内从 2,000 名候选者中找到了最佳抓握，并且对不同的真空夹具实现了 92.18% 的平均成功率，这在成功率和鲁棒性方面优于最先进的方法。